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Background: The literature describes teenagers as active users of social media, who seem to care about privacy, but who also reveal a considerable amount of personal information. There have been no studies of how they manage personal health information on social media.Objective: To understand how chronically ill teenage patients manage their privacy on social media sites.Design: A qualitative study based on a content analysis of semistructured interviews with 20 hospital patients (12-18 years).Results: Most teenage patients do not disclose their personal health information on social media, even though the study found a pervasive use of Facebook. Facebook is a place to be a "regular", rather than a sick teenager. It is a place where teenage patients stay up-to-date about their social life-it is not seen as a place to discuss their diagnosis and treatment. The majority of teenage patients don't use social media to come into contact with others with similar conditions and they don't use the internet to find health information about their diagnosis.Conclusions: Social media play an important role in the social life of teenage patients. They enable young patients to be "regular" teenagers. Teenage patients' online privacy behavior is an expression of their need for self-definition and self-protection. [ABSTRACT FROM AUTHOR]

Objective: There is increasing pressure to share health information and even make it publicly available. However, such disclosures of personal health information raise serious privacy concerns. To alleviate such concerns, it is possible to anonymize the data before disclosure. One popular anonymization approach is k-anonymity. There have been no evaluations of the actual re-identification probability of k-anonymized data sets.Design: Through a simulation, we evaluated the re-identification risk of k-anonymization and three different improvements on three large data sets.Measurement: Re-identification probability is measured under two different re-identification scenarios. Information loss is measured by the commonly used discernability metric.Results: For one of the re-identification scenarios, k-Anonymity consistently over-anonymizes data sets, with this over-anonymization being most pronounced with small sampling fractions. Over-anonymization results in excessive distortions to the data (i.e., high information loss), making the data less useful for subsequent analysis. We found that a hypothesis testing approach provided the best control over re-identification risk and reduces the extent of information loss compared to baseline k-anonymity.Conclusion: Guidelines are provided on when to use the hypothesis testing approach instead of baseline k-anonymity. [ABSTRACT FROM AUTHOR]

Before releasing personal health information for secondary uses, such as research or public health monitoring, organizations must de-identify the data they've collected. Several common heuristics are useful for this purpose, but they also have limitations. [ABSTRACT FROM PUBLISHER]

Objective: To provide a brief overview of artificial intelligence (AI) application within the field of eating disorders (EDs) and propose focused solutions for research., Method: An overview and summary of AI application pertinent to EDs with focus on AI's ability to address issues relating to data sharing and pooling (and associated privacy concerns), data augmentation, as well as bias within datasets is provided., Results: In addition to clinical applications, AI can utilize useful tools to help combat commonly encountered challenges in ED research, including issues relating to low prevalence of specific subpopulations of patients, small overall sample sizes, and bias within datasets., Discussion: There is tremendous potential to embed and utilize various facets of artificial intelligence (AI) to help improve our understanding of EDs and further evaluate and investigate questions that ultimately seek to improve outcomes. Beyond the technology, issues relating to regulation of AI, establishing ethical guidelines for its application, and the trust of providers and patients are all needed for ultimate adoption and acceptance into ED practice., Public Significance: Artificial intelligence (AI) offers a promise of significant potential within the realm of eating disorders (EDs) and encompasses a broad set of techniques that offer utility in various facets of ED research and by extension delivery of clinical care. Beyond the technology, issues relating to regulation, establishing ethical guidelines for application, and the trust of providers and patients are needed for the ultimate adoption and acceptance of AI into ED practice., (© 2024 Wiley Periodicals LLC.)